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Demand side management of a domestic dishwasher:

Demand side management of a domestic dishwasher: . Wind energy gains, financial savings and peak-time load reductions. Background. 40% of electricity demand will be supplied by renewable energy sources by 2020 Driven by the need to reduce GHG emissions

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Demand side management of a domestic dishwasher:

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  1. Demand side management of a domestic dishwasher: Wind energy gains, financial savings and peak-time load reductions

  2. Background • 40% of electricity demand will be supplied by renewable energy sources by 2020 • Driven by the need to • reduce GHG emissions • Become more independent of importing energy • 2007 imported fuels which accounted for 91% of annual consumption • Homegrown resources

  3. Renewable energy • Electricity generated from renewables: • 2% in 1995 • 14.4% in 2009 • 40% in 2020 • Wind energy making up 72% of that • Rise in ownership of domestic appliances • Rise in energy demand

  4. Demand side management (DSM) • Aims to reallocate demand to times of greater benefit • E.g. off peak times where prices will be lower • Can be 100% efficient: doesn’t require conversion of energy to and from an intermediary. • Timing of intended task which is displaced

  5. Case Study: Dishwasher • Zanussi ZDF231 • 5 young professional occupants • Parameters for DSM window: • Time when appliance was turned on • Load profile while operating • Time when cycle complete • Loads optimised for 3 factors: • Minimum cost • Maximum wind energy consumption • Minimum carbon emissions

  6. Results • Each setting achieved individual aim • E.g. when optimised for minimum cost, cost had the best results • “multi-objective gains” • Optimising the cost increased the wind energy demand • Increased carbon emissions by 2% • Stated that the reduction of carbon emissions should be aimed at the creation of energy rather than the usage in products

  7. Peat • Ireland history of reliance • 3x more CO₂ than natural gas • Periods of low demand had highest levels of peat use • Electricity generation: • 8.1% in 2008 • 0.002% by 2020 • Protection of habitats and cost of carbon emissions

  8. Conclusions • SMART appliances have the advantage of being able to input algorithims to predict periods of lowest cost (DSM) • Limitations: depending on parameters predictions can be filled with errors • Carbon emission calculations: no available data, Ireland’s TSO (Eirgrid) would have access • Leading to uncertainity of results and high margin for error

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